Quantitative Biology > Quantitative Methods

Abstract: Drug-drug interactions (DDI) can cause severe adverse drug reactions and pose
a major challenge to medication therapy. Recently, informatics-based approaches
are emerging for DDI studies. In this paper, we aim to identify key
pharmacological components in DDI based on large-scale data from DrugBank, a
comprehensive DDI database. With pharmacological components as features,
logistic regression is used to perform DDI classification with a focus on
searching for most predictive features, a process of identifying key
pharmacological components. Using univariate feature selection with chi-squared
statistic as the ranking criteria, our study reveals that top 10% features can
achieve comparable classification performance compared to that using all
features. The top 10% features are identified to be key pharmacological
components. Furthermore, their importance is quantified by feature coefficients
in the classifier, which measures the DDI potential and provides a novel
perspective to evaluate pharmacological components.